Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	18,897
2	New Jersey	17,461
3	Massachusetts	13,532
4	Rhode Island	13,276
5	District of Columbia	11,654
6	Connecticut	11,464
7	Delaware	9,206
8	Illinois	8,858
9	Louisiana	8,156
10	Maryland	7,898
11	Nebraska	6,386
12	Pennsylvania	5,651
13	Iowa	5,587
14	Michigan	5,488
15	South Dakota	5,183
16	Indiana	4,825
17	Mississippi	4,521
18	Virginia	4,420
19	Colorado	4,212
20	Georgia	3,900
21	Minnesota	3,781
22	New Mexico	3,350
23	North Dakota	3,224
24	Kansas	3,182
25	New Hampshire	3,086
26	Alabama	3,056
27	Tennessee	3,006
28	Ohio	2,778
29	Washington	2,758
30	Wisconsin	2,687
31	Utah	2,657
32	Nevada	2,583
33	California	2,455
34	Florida	2,408
35	North Carolina	2,288
36	Arizona	2,275
37	Missouri	2,003
38	Arkansas	1,997
39	Kentucky	1,991
40	South Carolina	1,976
41	Texas	1,948
42	Maine	1,542
43	Vermont	1,541
44	Oklahoma	1,539
45	Idaho	1,490
46	Wyoming	1,456
47	Puerto Rico	1,020
48	West Virginia	994
49	Oregon	936
50	Alaska	563
51	Montana	448
52	Hawaii	447

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Maryland	177
2	Illinois	171
3	District of Columbia	156
4	Delaware	149
5	Minnesota	136
6	Nebraska	119
7	Iowa	118
8	Connecticut	115
9	Massachusetts	115
10	Virginia	108
11	Rhode Island	103
12	Mississippi	93
13	Alabama	89
14	New Jersey	89
15	South Dakota	86
16	New York	79
17	North Carolina	74
18	Indiana	65
19	New Mexico	63
20	Louisiana	62
21	Wisconsin	62
22	North Dakota	61
23	Tennessee	57
24	Pennsylvania	53
25	California	52
26	Georgia	52
27	Utah	48
28	Ohio	47
29	Arkansas	46
30	Colorado	46
31	Nevada	46
32	New Hampshire	44
33	Arizona	43
34	Florida	35
35	South Carolina	34
36	Maine	31
37	Michigan	31
38	Washington	31
39	Missouri	27
40	Puerto Rico	24
41	Texas	24
42	Kansas	23
43	Wyoming	23
44	Oklahoma	20
45	Kentucky	15
46	West Virginia	14
47	Idaho	12
48	Oregon	6
49	Vermont	5
50	Alaska	3
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,497
2	New Jersey	1,254
3	Connecticut	1,049
4	Massachusetts	930
5	District of Columbia	623
6	Louisiana	578
7	Rhode Island	573
8	Michigan	524
9	Pennsylvania	402
10	Illinois	387
11	Maryland	380
12	Delaware	340
13	Indiana	294
14	Colorado	231
15	Mississippi	213
16	Georgia	171
17	Ohio	169
18	Minnesota	157
19	New Hampshire	154
20	New Mexico	152
21	Iowa	145
22	Virginia	141
23	Washington	141
24	Nevada	128
25	Alabama	115
26	Missouri	113
27	Arizona	110
28	Florida	104
29	California	96
30	Kentucky	90
31	Wisconsin	88
32	Vermont	86
33	South Carolina	85
34	Nebraska	80
35	Oklahoma	79
36	North Carolina	74
37	North Dakota	70
38	Kansas	68
39	Maine	58
40	South Dakota	56
41	Texas	52
42	Tennessee	48
43	Idaho	44
44	Puerto Rico	40
45	West Virginia	40
46	Arkansas	38
47	Oregon	35
48	Utah	30
49	Wyoming	20
50	Montana	14
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	District of Columbia	10
2	Connecticut	9
3	Massachusetts	9
4	Rhode Island	9
5	Maryland	5
6	New Jersey	5
7	New York	5
8	Illinois	4
9	Mississippi	4
10	Delaware	3
11	Ohio	3
12	Pennsylvania	3
13	Indiana	2
14	Iowa	2
15	Michigan	2
16	Minnesota	2
17	New Mexico	2
18	Virginia	2
19	Alabama	1
20	Arizona	1
21	Georgia	1
22	Louisiana	1
23	Nebraska	1
24	New Hampshire	1
25	South Carolina	1
26	Wisconsin	1
27	Alaska	0
28	Arkansas	0
29	California	0
30	Colorado	0
31	Florida	0
32	Hawaii	0
33	Idaho	0
34	Kansas	0
35	Kentucky	0
36	Maine	0
37	Missouri	0
38	Montana	0
39	Nevada	0
40	North Carolina	0
41	North Dakota	0
42	Oklahoma	0
43	Oregon	0
44	Puerto Rico	0
45	South Dakota	0
46	Tennessee	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,361	1	99
Dakota	Nebraska	80,995	2	99
Lincoln	Arkansas	74,862	3	99
Nobles	Minnesota	67,918	4	99
Lake	Tennessee	58,438	5	99
Davidson	Tennessee	6,945	264	91
Richland	South Carolina	3,286	673	78
Pierce	Washington	2,077	993	68
Orange	California	1,722	1159	63
York	South Carolina	1,231	1466	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Terrell	Georgia	3,048	1	99
Early	Georgia	2,846	2	99
Randolph	Georgia	2,803	3	99
Hancock	Georgia	2,601	4	99
Essex	New Jersey	1,996	5	99
Richland	South Carolina	152	566	81
Davidson	Tennessee	84	820	73
Pierce	Washington	82	832	73
Orange	California	41	1203	61
York	South Carolina	21	1468	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.